Evaluating the Feasibility and Efficacy of A Novel CBTi/SMT Treatment Protocol for Cardiac Rehab Patients: A Non-Randomized Pilot Trial
Bibliographic record
Abstract
INTRODUCTION: Cardiac patients and those with chronic medical conditions often suffer from comorbidities such as insomnia and mood disorders. Previous treatment protocols have focused on resolving symptoms of anxiety and depression in this population using Stress Management Training (SMT). However, these treatments have neglected the importance of sleep problems in these patients. This pilot trial sought to address this by examining the feasibility of a novel CBTi/SMT treatment protocol. METHODS: 42 participants attending a Cardiac Rehab (CR) exercise program registered in this 7-week non-randomized pilot trial. The primary objective of the pilot trial was to determine the feasibility of the protocol for retention and adherence rates. Secondarily, the authors sought to examine the potential efficacy of the program in terms of treating insomnia, depression, anxiety, emotion dysregulation, and arousal. RESULTS: 29 participants attended at least 1 class, with 21 participants completing the program. The average attendance for the program completers was 6 out of 7 classes (SD = 0.8) with four days of practice each week (SD = 1.6) for 33 minutes daily (SD = 16.8). Moreover, the number of participants meeting clinical threshold for insomnia, anxiety, and/or depression was significantly reduced at post-treatment and follow-up. Similarly, raw scores on the relevant scales were significantly reduced at both timepoints. CONCLUSION: This pilot trial provided preliminary evidence for the feasibility and efficacy of targeting sleep improvement with a combined CBTi/SMT protocol. This provides the groundwork for future RCTs to establish the effectiveness of targeting insomnia in a range of medical populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".